1. What is kibana?

Kibana is an open source analysis and visualization platform for Elasticsearch, which is used to search and view data interactively stored in the Elasticsearch index. Using Kibana, you can perform advanced data analysis and display through various charts.
Kibana makes big data easier to understand. It is easy to operate, and the browser-based user interface can quickly create a dashboard
(dashboard) to display Elasticsearch query dynamics in real time.

2. Kibana installation

First, root permission is required, and a kibana folder is created during mkdir.

su root

mkdir /usr/local/kibana

Transfer to Linux with Xftp.

Then decompress. Go to the transmission folder, find the tar package and decompress it.

tar -zxvf kibana-7.4.0-linux-x86_64.tar.gz -C /usr/local/kibana

3. Modify kibana configuration

sudo vi /usr/local/kibana/kibana-7.4.0-linux-x86_64/config/kibana.yml

You can modify these five places: enter vim, then click i to operate, save wq!

Do not close the previous es service, it needs to be used here, just close it and open it directly.

server.port : http access port

server.host : ip address, 0.0.0.0 means remote access

server.name : kibana service name

elasticsearch.hosts ： elasticsearch 地址

elasticsearch.requestTimeout : request elasticsearch timeout, the default is 30000 , here can be set according to the situation

4. Start kibana

It is not recommended to use the root user to start here, if you want to use the root user, you need to add the --allow-root parameter.

 cd /usr/local/kibana/kibana-7.4.0-linux-x86_64/bin

./kibana --allow-root

The startup is successful, and there are warnings to ignore.

5. Access kibana

192.168.179.128:5601/

You need to pay attention to the ip of your own virtual machine, and then the port is 5601.

Click dev tools on the left side of the main page

6. Elasticsearch concept

Index (index)
The place where ElasticSearch stores data can be understood as the database concept in a relational database.

Mapping (mapping)
Mapping defines the type of each field, the tokenizer used by the field, and so on. It is equivalent to the table structure in relational database.

Document (document)
The smallest data unit in Elasticsearch, often displayed in json format. A document is equivalent to a row of data in a relational database.

Inverted Index
An inverted index consists of a list of all unique words in a document, and for each word there is a list of document ids that contain it.

Type (type)
A type is like a class of tables. Such as user table, role table, etc. In Elasticsearch7.X, the default type is _doc

7. Operation

(1) Create an index

PUT student

GET /student/_mapping

(2) Create a mapping

PUT /student/_mapping
{
  "properties":{
    "name":{
      "type":"text"
    },
    "age":{
      "type":"integer"
    }
    
  }
}

GET /student/_mapping

(3) Add document, specify id

POST /student/_doc/1
{
  "name":"曹俊",
  "age":24
}

GET /student/_doc/1

Add document without specifying id

POST /student/_doc
{
  "name":"谭咏麟",
  "age":55
}

query all documents

GET /student/_search

GET /student/_doc/_o4ZqIcBhbyZEPhCCkXJ

(4) Delete the document

DELETE /student/_doc/1

GET /student/_search

Query all documents and find that they have been deleted.

{
  "took" : 811,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "student",
        "_type" : "_doc",
        "_id" : "_o4ZqIcBhbyZEPhCCkXJ",
        "_score" : 1.0,
        "_source" : {
          "name" : "谭咏麟",
          "age" : 55
        }
      }
    ]
  }
}

(2) Introduction and installation of ElasticSearch auxiliary tool Kibana